This disclosure relates generally to clinical documentation and, specifically, to automated techniques for recognizing clinical indicators of disease. In particular, the disclosure relates to natural language processor techniques for clinical document review, including automated recognition of disease indicators.
Broadly speaking, clinical documentation improvement (CDI) initiatives seek to improve the quality of provider documentation in order to better reflect the services rendered and more accurately represent the complete patient encounter. CDI programs can benefit many clinical and administrative functions in healthcare delivery, including coding, quality measures reporting, care management, outcomes analysis, risk analysis, and subsequent care decisions. These benefits are derived from clearer and more complete clinical documentation.
CDI can play an important role in the transition to new medical classification and coding systems, for example ICD-10, ICD-11, and other revisions to the ICD medical classification system (International Statistical Classification of Diseases and Related Health Problems) by the World Health Organization. With greater specificity and increased scope for both diagnosis and procedure coding, CDI programs in provider organizations can address the potential gap between the content of current clinical documentation and the level of detail required for new and updated ICD codes.
Combining the existing opportunities to realize clinical and financial benefits with the magnitude of the ICD changes, providers seek new and more accurate solutions to help improve documentation. These solutions should be efficient, with minimal disruption to the physician workflow, and they should have specific, measurable benefits.
In this disclosure, computer-assisted natural language processing (NLP) technology is applied to transform existing CDI programs and coding solutions. Like coding, CDI programs can be labor intensive and require highly trained specialists to execute. CDI also has a unique set of challenges, because, while similar to coding in some respects, CDI requires a different approach to medical records review in order to identify potential gaps in the clinical story.
A high level of both clinical and processing knowledge is required to identify these clinical gaps and other improvement scenarios, with an advanced understanding of which areas have the greatest potential for development from both clinical and financial standpoints. With existing programs it is not possible to effectively review every chart and patient encounter in order to identify and select the greatest opportunities for improvement. Where physician queries must be communicated back to the provider, moreover, it is notoriously difficult to integrate this feedback into the provider workflow using standard communications mechanisms such as email and fax technology.
To transform existing CDI programs, more advanced technology should be applied to identify particular cases that exhibit opportunities for improvement in clinical care, provide structured models of clinical evidence to support consistent decisions by CDI staff, and incorporate new tools to improve construction of specific queries, more efficiently communicate these queries to clinicians, and monitor responses to improve key performance measures. This disclosure describes factors relevant to the alignment of NLP technology and CDI solutions to accomplish these goals, including: (1) more accurate extraction of clinical evidence from medical records for automated case-finding, (2) an improved clinical information model that supports consistent query decisions, and (3) compositional approaches to NLP, which can recognize more sophisticated CDI scenarios.